Low-complexity WLMMSE channel estimator for MB-OFDM UWB systems

Abstract

Widely linear (WL) minimum mean square error (MMSE) channel estimation scheme have been proposed for multiband orthogonal frequency division multiplexing ultra-wideband (MB-OFDM UWB) systems dealing with non-circular signals. This WLMMSE channel estimation scheme provides significant performance gain, but it presents a high computational complexity compared with the linear one. In this paper, we derive an adaptive WLMMSE channel estimation scheme that significantly reduces the computational complexity. The complexity reduction is done in two stages. The first stage consists of a real evaluation of the WLMMSE channel estimator and the second stage follows with a reduced-rank filtering based on the singular value decomposition (SVD). Computational complexity evaluation shows that the proposed low-rank real-valued WLMMSE channel estimator has computation cost comparable with the linear MMSE. Additionally, simulations of the bit error rate (BER) performance show comparable performance with the WLMMSE channel estimator especially at high signal-to-noise ratio (SNR).

Widely linear channel estimation for LD‐STBC MB‐OFDM UWB systems

“…Equations (28), (30), (31), and (32) into (26) give the WLMMSE channel estimator in the general case which suppose that the transmitted signals are selected from improper constellation.…”

“…The WL processing has gained a growing interest in the past few years. It has been shown that it provides performance improvement in both cases: when dealing with improper signal constellations and when STBCs are used on the transmitter side . In this paper, we focus on the second case.…”

“…However, the WL processing has not been exploited for channel estimation purposes in MIMO systems. But, a low‐rank real‐valued WLMMSE (Lr‐RWLMMSE) channel estimator has been proposed for Single‐Input Single‐Output (SISO) MB‐OFDM UWB system dealing with improper signal constellation . It has been shown that the proposed estimator provides significant performance gain with enormous reduction of computational complexity.…”